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A NEW INSURANCE LOSS MODEL TO PROMOTE CATASTROPHE INSURANCE MARKET IN INDIA AND PAKISTAN

机译:促进印度和巴基斯坦灾难性保险市场的新保险损失模型

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Catastrophe loss modelling has been under rapid development in the recent years. This is mainly due to significant demands for natural catastrophe models and their applications in the financial and insurance industry. Computer risk models today are used to evaluate potential losses from future events and provide facilities for better controlling exposure to potential losses. The first generation of earthquake loss model for India subcontinent was developed in the late 1990's and has been used by many insurance and reinsurance companies for various insurance portfolio analyses. In this paper the methodology and preliminary results for a new earthquake model for this region is presented. The seismic hazard, the built environment inventory and the building vulnerabilities are probabilistically convoluted to estimate probabilistic losses. Such results once implemented in user friendly financial software, are used by insurance and reinsurance companies to estimate their potential exposure to seismic hazards. The main advantages of this model are its high resolution stochastic event set, detailed seismic model, and a range of vulnerability functions describing various building types and contents. The new model uses the latest information on regional seismotectonic to develop a new regional seismic hazard model for the Himalayan belt as well as stable Indian shield. This model benefits from a high geographical resolution for the underlying administrative units to capture detailed variation of seismic hazard and to enhance the modelling of soil effect.
机译:近年来,巨灾损失建模一直在快速发展。这主要是由于对自然灾害模型及其在金融和保险行业中的应用的巨大需求。今天的计算机风险模型用于评估未来事件的潜在损失,并提供更好地控制潜在损失风险的工具。印度次大陆的第一代地震损失模型是在1990年代后期开发的,已被许多保险和再保险公司用于各种保险组合分析。本文介绍了该地区新的地震模型的方法和初步结果。地震危险性,建筑环境清单和建筑物脆弱性被卷积起来以估计概率损失。此类结果一旦在用户友好型财务软件中实现,便被保险和再保险公司用来估计其潜在的地震危险。该模型的主要优点是其高分辨率的随机事件集,详细的地震模型以及描述各种建筑物类型和内容的一系列易损性函数。新模型利用有关区域地震构造的最新信息,为喜马拉雅带和稳定的印度盾构开发了新的区域地震灾害模型。该模型得益于基础行政单位的高地理分辨率,可以捕获地震灾害的详细变化并增强对土壤影响的建模。

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